Triple
T3068701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abu Dhabi Grand Prix |
E62166
|
entity |
| Predicate | tyreSupplier |
P30565
|
FINISHED |
| Object | Pirelli |
E188558
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Pirelli | Statement: [Abu Dhabi Grand Prix, tyreSupplier, Pirelli]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pirelli Context triple: [Abu Dhabi Grand Prix, tyreSupplier, Pirelli]
-
A.
Pirelli
chosen
Pirelli is an Italian multinational company best known as one of the world’s leading manufacturers of high-performance tyres, particularly in motorsport and premium road vehicles.
-
B.
Michelin
Michelin is a major French multinational tire manufacturer renowned for its tires, travel guides, and the Michelin star restaurant rating system.
-
C.
Bridgestone
Bridgestone is a global tire and rubber company headquartered in Japan, known for its extensive involvement in motorsports and major sports sponsorships.
-
D.
Nokian Tyres
Nokian Tyres is a Finnish tire manufacturer best known for its high-quality winter and all-weather tires designed for challenging Nordic conditions.
-
E.
Continental
Continental is a major German automotive manufacturing company best known for producing tires, braking systems, and other vehicle components.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad85793e5c8190a358049bc4a98d8c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada0ffcc208190962cc9edcbf43c31 |
completed | March 8, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1ef1972e08190942a068c0c563e52 |
completed | March 11, 2026, 10:39 p.m. |
Created at: March 8, 2026, 3:02 p.m.